Our GPU-optimized 3D SIFT-CNN method was first described and used in the context of brain MRI analysis [1]}. The original 3D SIFT-Rank method has been used in a variety of keypoint applications analyzing 3D images of the human body. These include modeling the development of the infant human brain over time [2]}, keypoint matching between different image modalities having multi-modal, non-linear intensity relationships [3]}, robust alignment of lung scans [4]}, kernel density formulation for efficient memory-based indexing from large image datasets [5]}, efficient whole-body medical image segmentation via keypoint matching and label transfer [6]}, [7]}, alignment of 4D cardiac ultrasound sequences [8]}, alignment of 3D ultrasound volumes for panoramic stitching [9]}, identifying family members from brain MRI [10]}, and large-scale population studies using multiple neurological MRI modalities  [11]}. The 3D SIFT-Rank method was also used for robust image alignment in the context of image-guided neurosurgery [12]}, including non-rigid registration [13]} based on regularization of deformation fields via thin-plane splines and finite element models [14]}, [15]}, and robust filtering of image-to-image correspondences using the variogram [16]}. Alternative keypoint descriptors have been proposed based on vector data, e.g. diffusion MRI histograms of the human brain [17]}. The Jaccard distance between feature sets was introduced to characterize the bag-of-feature manifold, to automatically flag errors in large public MRI datasets [18]}, [19]}, and most recently the first approach to identify family members from brain MRI [20]}.
